Thomas Reid writes about OpenAI's new Decisions API, a dedicated endpoint that makes fast, typed decisions such as classifying support requests, detecting missing information in documents, and inspecting product images. The API supports predicate, choice, and score question types and runs on the gpt-6-luna LLM, claiming up to 10x speed over standard LLM calls. Through three Python examples, the article compares the Decisions API to TypesafeAI's JEV model, concluding that Decisions has the edge on image handling while JEV leads on speed and input cost.
- The API costs $0.10 per million input tokens with no charges for output, cache reads, or cache writes.
- JEV costs $0.042 per million input tokens, making it 58% cheaper on input.
- Images must be sent as inline data URLs; standard web URLs and file IDs are not accepted.
- The endpoint supports up to 128 images per request.
- The API costs $0.10 per million input tokens with no charges for output, cache reads, or cache writes.
- JEV costs $0.042 per million input tokens, making it 58% cheaper on input.
- Images must be sent as inline data URLs; standard web URLs and file IDs are not accepted.
- The endpoint supports up to 128 images per request.
Fallahpour and colleagues write about Memorilla, a memory system that compresses an entire document collection into a fixed set of sixteen query-dependent memory tokens injected into a frozen LLM's input embedding space, outperforming baselines like Mem0 and MemoRAG on five of eight benchmarks spanning personalization, long-document, and knowledge-intensive question answering.
- Memorilla encodes documents once into reusable embeddings, then learns to retrieve and place query-specific information directly in the LLM's input embedding space.
- On FactKG it reaches 84.33% accuracy versus 80.43% with full-context inference, while using 108x fewer tokens to represent the source documents.
- In a TextWorld agentic environment, Memorilla raises average success rate from 18.6% (full-history in-context learning) to 24.2% and reduces the agent's tendency to re-explore rooms it has already visited.
- The work is positioned as a step toward continually learning systems, where the document count grows as an agent accumulates past turns.
- Memorilla encodes documents once into reusable embeddings, then learns to retrieve and place query-specific information directly in the LLM's input embedding space.
- On FactKG it reaches 84.33% accuracy versus 80.43% with full-context inference, while using 108x fewer tokens to represent the source documents.
- In a TextWorld agentic environment, Memorilla raises average success rate from 18.6% (full-history in-context learning) to 24.2% and reduces the agent's tendency to re-explore rooms it has already visited.
- The work is positioned as a step toward continually learning systems, where the document count grows as an agent accumulates past turns.
Natalie Wolchover writes that the 2014 invention of the attention mechanism by Dzmitry Bahdanau, Yoshua Bengio, and Kyunghyun Cho unlocked scalable machine translation, which became the foundation for transformer architectures and the broader large language model revolution. Wolchover examines how attention functions as a mathematical operation allowing neural networks to link words and build context, and whether this mechanism represents the core of intelligence. She concludes that while language modeling is so demanding it may be "AI-complete," human thought likely relies on non-linguistic processes as well, and that current transformer architectures remain far less efficient than the human brain at accomplishing similar tasks.
- Bahdanau was a 23-year-old intern at the University of Montreal in 2014 who described attention as "just obviously the right thing to do."
- The Google paper "Attention Is All You Need" (2017) has been cited more than 250,000 times.
- Llion Jones, co-author of "Attention Is All You Need" and CTO of Sakana AI, now believes attention is not sufficient for truly masterful language modeling and that a fundamentally different paradigm is needed.
- A 2024 Nature study suggests human thought activates more brain regions than just language networks, indicating intelligence is not purely linguistic.
- Bahdanau was a 23-year-old intern at the University of Montreal in 2014 who described attention as "just obviously the right thing to do."
- The Google paper "Attention Is All You Need" (2017) has been cited more than 250,000 times.
- Llion Jones, co-author of "Attention Is All You Need" and CTO of Sakana AI, now believes attention is not sufficient for truly masterful language modeling and that a fundamentally different paradigm is needed.
- A 2024 Nature study suggests human thought activates more brain regions than just language networks, indicating intelligence is not purely linguistic.
Debashis Das writes that M5Stack has released the PaperDIY series, an updated version of its 2020 M5Paper kit that pairs an ESP32-S3 MCU with a 4.7-inch e-paper display supporting 16 levels of gray. The product is available in two versions: a Complete Kit that ships factory-assembled in a 3D-printed enclosure, and an Assembly Kit designed for custom projects. Both boards include a microSD card slot, a built-in 140mAh battery, and a custom M5PM1 low-power management chip.
- The display uses an 8-bit parallel interface, enabling direct control without a secondary display controller
- M5Stack provides factory firmware source code based on ESP-IDF, including examples for battery monitoring and Wi-Fi scanning
- A critical warning notes that mounting screws must have a thread length under 3mm to avoid permanently damaging the e-paper display
- The Assembly Kit retails for $25, while the Complete Kit costs $29.90
- The display uses an 8-bit parallel interface, enabling direct control without a secondary display controller
- M5Stack provides factory firmware source code based on ESP-IDF, including examples for battery monitoring and Wi-Fi scanning
- A critical warning notes that mounting screws must have a thread length under 3mm to avoid permanently damaging the e-paper display
- The Assembly Kit retails for $25, while the Complete Kit costs $29.90
Choi In-jun writes about how AI tools are enabling rapid reverse engineering of commercial software, threatening established players like Adobe and Microsoft. Developer Brandon Thomas used AI to replicate features of Adobe's creative suite and Microsoft Office programs, releasing them for free and prompting HSBC to downgrade Adobe's stock rating. A new open-source tool called REA (Reverse Engineer Anything) connects AI coding assistants with specialized reverse engineering tools, dramatically reducing the time and cost needed to analyze and replicate software functionality.
- A Chosun reporter demonstrated that REA could analyze an open-source document editor in ~7 seconds, with AI coding tools then recreating core editing features and a working interface in under 7 minutes total.
- The NSA's Ghidra, a reverse engineering tool hidden for nearly 20 years and released publicly in 2019, previously made analysis impractical due to missing comments and massive code size; AI has largely overcome this barrier.
- A South Korean developer released "Tangle 2026," which can read and edit HWP/HWPX files used by Hancom's Hangul, representing a domestic attempt at AI-driven software replication.
- Opinions in the industry are split: Dutch developer Pieter Levels argues users will build only the features they need rather than pay subscriptions, while Brazilian developer Fábio Akita counters that AI-generated alternatives contain errors and cannot fully replicate complex commercial products.
- South Korea's Copyright Act permits code analysis for compatibility or operational understanding but prohibits using obtained information beyond those purposes, setting the stage for legal disputes as replication attempts increase.
- A Chosun reporter demonstrated that REA could analyze an open-source document editor in ~7 seconds, with AI coding tools then recreating core editing features and a working interface in under 7 minutes total.
- The NSA's Ghidra, a reverse engineering tool hidden for nearly 20 years and released publicly in 2019, previously made analysis impractical due to missing comments and massive code size; AI has largely overcome this barrier.
- A South Korean developer released "Tangle 2026," which can read and edit HWP/HWPX files used by Hancom's Hangul, representing a domestic attempt at AI-driven software replication.
- Opinions in the industry are split: Dutch developer Pieter Levels argues users will build only the features they need rather than pay subscriptions, while Brazilian developer Fábio Akita counters that AI-generated alternatives contain errors and cannot fully replicate complex commercial products.
- South Korea's Copyright Act permits code analysis for compatibility or operational understanding but prohibits using obtained information beyond those purposes, setting the stage for legal disputes as replication attempts increase.
Qwen publishes official GGUF weights for its 8-billion-parameter vision-language model, Qwen3-VL-8B-Instruct, allowing local inference of image, video, and text workloads through common tools like llama.cpp, Ollama, LM Studio, and vLLM without needing a custom Transformers environment. The model supports a 256K-token context window, 32-language OCR, 2D/3D grounding, GUI agent capabilities, and video timestamp localization, all under an Apache 2.0 license.
- The language model is separated from a multimodal projector (mmproj) file, letting users mix precisions such as a 4-bit language model with an F16 projector for better OCR accuracy
- The Q4_K_M language model plus a quantized projector can run on 16 GB unified-memory systems, though the full 256K context window is impractical on that hardware
- Benchmark scores include DocVQA 96.1, MMMU 69.6, and MMLU-Pro 68.6
- The language model is separated from a multimodal projector (mmproj) file, letting users mix precisions such as a 4-bit language model with an F16 projector for better OCR accuracy
- The Q4_K_M language model plus a quantized projector can run on 16 GB unified-memory systems, though the full 256K context window is impractical on that hardware
- Benchmark scores include DocVQA 96.1, MMMU 69.6, and MMLU-Pro 68.6
Asif Razzaq writes that Google Research has open-sourced Harness², a framework that improves agent performance by recursively refining the surrounding harness—prompts, tools, memory, and orchestration—while keeping model weights frozen. It operates without ground-truth labels, using a Propose, Probe, Compose cycle to test candidate harness edits against observed behavior.
- Weaker executors gained more; GLM-5 beat base Gemini 3.5 Flash on WorkBuddy-Bench after one refinement step
- Parallel recursion at k=5 lifted Gemini under OpenCode to 80.8 on WorkBuddy
- The framework extends the 7-file split from earlier Agentic Harness Engineering work by 1 component
- Requires Linux, Python 3.12+, uv, and Vertex AI for model-backed runs
- Weaker executors gained more; GLM-5 beat base Gemini 3.5 Flash on WorkBuddy-Bench after one refinement step
- Parallel recursion at k=5 lifted Gemini under OpenCode to 80.8 on WorkBuddy
- The framework extends the 7-file split from earlier Agentic Harness Engineering work by 1 component
- Requires Linux, Python 3.12+, uv, and Vertex AI for model-backed runs
The T5 e-Paper Basic is a $34.04 development board featuring an ESP32-S3 microcontroller, 16MB flash, 8MB PSRAM, and a 4.7-inch ultra-low-power e-paper display with 540x960 resolution and 16 gray levels.
- Driver IC for the display is ED047TC1
- Includes I2C AXP2602 battery gauge, XL9555 I/O expander, and TF card slot
- Ships from local warehouses in Germany, USA, and Canada
- Driver IC for the display is ED047TC1
- Includes I2C AXP2602 battery gauge, XL9555 I/O expander, and TF card slot
- Ships from local warehouses in Germany, USA, and Canada
Sofia Lindström writes that cloud GPU pricing has diverged sharply in 2026, with AWS offering the lowest single-H100 on-demand rate at $6.88/hr while Azure's eight-GPU node pushes per-GPU costs to $12.29/hr. Azure leads in spot discounts at roughly 80% and object storage at $0.0184/GB-month, but its GPU premium makes it the priciest option for accelerator-heavy workloads.
- Google Cloud enforces an eight-GPU on-demand minimum for H100s, forcing small teams to pay for six unused accelerators
- Specialist GPU clouds like RunPod price H100s at $2.89/hr, a 2.4x discount versus AWS's on-demand rate
- Secondary charges (storage, public IP, egress) add 25-40% on top of advertised VM rates across all three hyperscalers
- AWS P5 GPU quota approval requires written justification and takes 3-7 business days
- Google Cloud enforces an eight-GPU on-demand minimum for H100s, forcing small teams to pay for six unused accelerators
- Specialist GPU clouds like RunPod price H100s at $2.89/hr, a 2.4x discount versus AWS's on-demand rate
- Secondary charges (storage, public IP, egress) add 25-40% on top of advertised VM rates across all three hyperscalers
- AWS P5 GPU quota approval requires written justification and takes 3-7 business days
Alex Davis writes that natural language processing offers product data scientists a complementary tool to strengthen traditional behavioral and transactional models. By extracting sentiment, topics, and semantic embeddings from support tickets, reviews, and survey responses, teams can improve accuracy across churn prediction, lifetime value, segmentation, and demand forecasting. The article emphasizes that text shifts often occur before numerical usage data does, allowing NLP-derived features to serve as early signals in prediction pipelines.
- The workflow involves aggregating NLP outputs into features that join directly onto existing behavioral tables for model training
- Embeddings help solve the cold-start problem for new customers and items in recommendation and CLV systems
- Simple A/B comparison tests between behavioral-only and behavioral-plus-text models validate whether text features add predictive value
- The workflow involves aggregating NLP outputs into features that join directly onto existing behavioral tables for model training
- Embeddings help solve the cold-start problem for new customers and items in recommendation and CLV systems
- Simple A/B comparison tests between behavioral-only and behavioral-plus-text models validate whether text features add predictive value
